No abstract

Rating scales, such as Likert scales, are very common in marketing research, customer satisfaction studies, psychometrics, opinion surveys, population studies, and numerous other fields. We recommend diverging stacked bar charts as the primary graphical display technique for Likert and related scales. We also show other applications where diverging stacked bar charts are useful. Many examples of plots of Likert scales are given. We discuss the perceptual and programming issues in constructing these graphs. We present two implementations for diverging stacked bar charts. Most examples in this paper were drawn with the likert function included in the HH package in R. We also have a dashboard in Tableau.

Cover illustration: Cover art is a variation of Figure 14.14d. The data source is (Williams, 2001).

Patient safety has always been a primary focus in the development of new pharmaceutical products. The predominant method for statistical evaluation and interpretation of safety data collected in a clinical trial is the tabular display of descriptive statistics. There is a great opportunity to enhance evaluation of drug safety through the use of graphical displays, which can convey multiple pieces of information concisely and more effectively than can tables. Graphs can be used in an exploratory setting to help identify emerging safety signals, or in a confirmatory setting as a tool to elucidate known safety issues. We developed several graphical displays for routine safety data collected during a clinical trial, covering a broad range of graphical techniques, and illustrate here 10 specific graphical designs, many of which display the data along with statistics derived from them. Two are simple plots, comparing distributions in the form of boxplots or cumulative plots, and four more display data and summaries over time, comparing information from two groups in terms of distribution (with boxplots), cumulative incidence, hazard, or simply means with error bars. The other four are multi-panel displays: one-dimensional and two-dimensional arrays of scatterplots, a trellis of individual profiles, and a paired dotplot displaying risk together with relative risk. The displays focus on key safety endpoints in clinical trials including the QT interval from electrocardiograms, laboratory measurements for detecting hepatotoxicity, and adverse events of special interest. We discuss in detail the statistical and graphical principles underlying the production and interpretation of the displays.

In this chapter we discuss bivariate discrete distributions. Bivariate means that there are two factors (categorical variables) defining cells. The response values are frequencies, that is, counts or instances of observations, at each cell.It is convenient to arrange such data in a contingency table, that is, a table with r rows representing the possible values of one categorical variable and c columns representing the possible values of the other categorical variable. Each of the rc cells of the table contains an integer, the number of observations having the levels of the two variables specified by the cell location. We give extra attention to the special case where r = c = 2, that is, a 2 × 2 contingency table.